Black, E., Maidment, R. I., Rees, E., and Nderitu, E.: A new drought model for disaster risk management in the Punjab, Sindh and Baluchistan provinces of Pakistan, Front. Climate, 6, 1332233, https://doi.org/10.3389/fclim.2024.1332233, 2024c.
Boult, V. L., Asfaw, D. T., Young, M., Maidment, R., Mwangi, E., Ambani, M., Waruru, S., Otieno, G., Todd, M. C., and Black, E.: Evaluation and validation of TAMSAT-ALERT soil moisture and WRSI for use in drought anticipatory action, Meteorol. Appl., 27, e1959, https://doi.org/10.1002/met.1959, 2020.
Boult, V. L., Black, E., Abdillahi, H. S., Bailey, M., Harris, C., Kilavi, M., Kniveton, D., MacLeod, D., Mwangi, E., Otieno, G., and Rees, E.: Towards drought impact-based forecasting in a multi-hazard context, Clim. Risk Manage., 35, 100402, https://doi.org/10.1016/j.crm.2022.100402, 2022.
Brown, M., Black, E., Asfaw, D., and Otu-Larbi, F.: Monitoring drought in Ghana using TAMSAT-ALERT: A new decision support system, Weather, 72, 201–205, https://doi.org/10.1002/wea.3033, 2017.
Dasgupta, A., Arnal, L., Emerton, R., Harrigan, S., Matthews, G., Muhammad, A., O'Regan, K., Pérez-Ciria, T., Valdez, E., van Osnabrugge, B., and Werner, M.: Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop, J. Flood Risk Manage., e12880, https://doi.org/10.1111/jfr3.12880, 2023.
David, D. L.: Detecting agricultural drought risks: A case study of the rice crop (Oryza sativa) and the TAMSAT-ALERT system in Guyana, Meteorol. Appl., 30, e2149, https://doi.org/10.1002/met.2149, 2023.
Diro, G. T., Black, E., and Grimes, D. I. F.: Seasonal forecasting of Ethiopian spring rains, Meteorol. Appl., 15, 73–83, https://doi.org/10.1002/met.63, 2008.
Ellis, J. and Black, E.: General TAMSAT-ALERT v1.2.1, Zenodo [code], https://doi.org/10.5281/zenodo.10955490, 2024a.
Ellis, J. and Black, E.: general-tamsat-alert 1.2.2,
https://pypi.org/project/general-tamsat-alert/, last access: 17 November 2024b.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, Sci. Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66, 2015.
Gissila, T., Black, E., Grimes, D. I. F., and Slingo, J. M.: Seasonal forecasting of the Ethiopian summer rains, Int. J. Climatol., 24, 1345–1358, https://doi.org/10.1002/joc.1078, 2004.
Goddard, L. and Gershunov, A.: Impact of El Niño on weather and climate extremes, in: El Niño Southern Oscillation in a Changing Climate, edited by: McPhaden, M. J., Santoso, A., and Cai, W., American Geophysical Union (New Jersey, USA) and John Wiley & Sons, Inc. (Washington DC, USA), https://doi.org/10.1002/9781119548164.ch16, 361–375, 2020.
Gudoshava, M., Wanzala, M., Thompson, E., Mwesigwa, J., Endris, H. S., Segele, Z., Hirons, L., Kipkogei, O., Mumbua, C., Njoka, W., and Baraibar, M.: Application of real-time S2S forecasts over Eastern Africa in the co-production of climate services, Clim. Serv., 27, 100319, https://doi.org/10.1016/j.cliser.2022.100319, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hirons, L., Thompson, E., Dione, C., Indasi, V. S., Kilavi, M., Nkiaka, E., Talib, J., Visman, E., Adefisan, E. A., de Andrade, F., and Ashong, J.: Using co-production to improve the appropriate use of sub-seasonal forecasts in Africa, Clim. Serv., 23, 100246, https://doi.org/10.1016/j.cliser.2021.100246, 2021.
Hirons, L., Wainwright, C. M., Nying'uro, P., Quaye, D., Ashong, J., Kiptum, C., Opoku, N. K., Thompson, E. M., and Lamptey, B.: Experiences of co-producing sub-seasonal forecast products for agricultural application in Kenya and Ghana, Weather, 78, 148–153, https://doi.org/10.1002/wea.4324, 2023.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., and Zhu, Y.: The NCEP/NCAR 40-year reanalysis project, in: Renewable energy, Routledge, Routledge, London, https://doi.org/10.4324/9781315793245, 2018.
Kay, G., Dunstone, N. J., Smith, D. M., Betts, R. A., Cunningham, C., and Scaife, A. A.: Assessing the chance of unprecedented dry conditions over North Brazil during El Niño events, Environ. Res. Lett., 17, 064016, https://doi.org/10.1088/1748-9326/ac6cd3, 2022.
Kim, K., Chowdhury, R., Pant, P., Yamashita, E., and Ghimire, J.: Assessment of ENSO risks to support transportation resilience, Prog. Disaster Sci., 12, 100196, https://doi.org/10.1016/j.pdisas.2021.100196, 2021.
Lavers, D. A., Simmons, A., Vamborg, F., and Rodwell, M. J.: An evaluation of ERA5 precipitation for climate monitoring, Q. J. Roy. Meteor. Soc., 148, 3152–3165, https://doi.org/10.1002/qj.4363, 2022.
Maidment, R. and Black, E.: General TAMSAT-ALERT example datasets,
https://gws-access.jasmin.ac.uk/public/tamsat/tamsat_alert/gmd_paper/datasets.zip, last access: 21 November 2024.
Maidment, R. I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex, H., Allan, R. P., Stein, T., Nkonde, E., Senkunda, S., and Alcántara, E. M. U.: A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa, Sci. Data, 4, 170063, https://doi.org/10.1038/sdata.2017.63, 2017.
Manzanas, R.: Assessment of model drifts in season al forecasting: Sensitivity to ensemble size and implications for bias correction, J. Adv. Model. Earth Sy., 12, e2019MS001751, https://doi.org/10.1029/2019MS001751, 2020.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, 17–22 January 1993, Anaheim, California, USA, pp. 179–183, 1993.
Nobre, G. G., Muis, S., Veldkamp, T. I., and Ward, P. J.: Achieving the reduction of disaster risk by better predicting impacts of El Niño and La Niña, Prog. Disaster Sci., 2, 100022, https://doi.org/10.1016/j.pdisas.2019.100022, 2019.
Novella, N. S. and Thiaw, W. M.: A seasonal rainfall performance probability tool for famine early warning systems, J. Appl. Meteorol. Clim., 55, 2575–2586, https://doi.org/10.1175/JAMC-D-16-0102.1, 2016.
Parker, D. E., Legg, T. P., and Folland, C. K.: A new daily central England temperature series, 1772–1991, Int. J. Climatol., 12, 317–342, https://doi.org/10.1002/joc.3370120402, 1992.
Reason, C.: Climate of Southern Africa. Oxford Research Encyclopedia of Climate Science,
https://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-513 (last access: 17 November 2024), 2017.
Ren, X., Li, X., Ren, K., Song, J., Xu, Z., Deng, K., and Wang, X.: Deep learning-based weather prediction: a survey, Big Data Res., 23, 100178, https://doi.org/10.1016/j.bdr.2020.100178, 2021.
Salvi, K., Ghosh, S., and Ganguly, A. R.: Credibility of statistical downscaling under nonstationary climate, Clim. Dynam., 46, 1991–2023, https://doi.org/10.1007/s00382-015-2688-9, 2016.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.: GPCC full data reanalysis version 7.0: Monthly land-surface precipitation from rain gauges built on GTS based and historic data, Deutscher Wetterdienst (DWD) [data set], Germany, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050, 2016.
Shukla, S., McNally, A., Husak, G., and Funk, C.: A seasonal agricultural drought forecast system for food-insecure regions of East Africa, Hydrol. Earth Syst. Sci., 18, 3907–3921, https://doi.org/10.5194/hess-18-3907-2014, 2014.
Siyal, G. E. A., Kazmi, S. M. A., and Zahara, M.: Understanding El-Niño: Tracing its impacts in Sindh, Pakistan, in: 70 Years of Development: the Way Forward, edited by Aneel, S., Haroon, U. T., and Niazi, I., extracted from report issued by the Sustainable Policy Institute,
https://www.jstor.org/stable/resrep24393.14 (last access: 26 March 2024), 2019.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO crop model to simulate yield response to water: I. Concepts and underlying principles, Agron. J., 101, 426–437, https://doi.org/10.2134/agronj2008.0139s, 2009.
Turner, W. A., Husak, G., Funk, C., Roberts, D. A., and Jones, C.: An improved climatological forecast method for projecting end-of-season Water Requirement Satisfaction Index, J. Hydrometeorol., 23, 1281–1295, https://doi.org/10.1175/JHM-D-21-0232.1, 2022.
Yang, W., Kogan, F., Guo, W., and Chen, Y.: A novel re-compositing approach to create continuous and consistent cross-sensor/cross-production global NDVI datasets, Int. J. Remote Sens., 42, 6023–6047, https://doi.org/0.1080/01431161.2021.1934597, 2021.
Young, M., Heinrich, V., Black, E., and Asfaw, D.: Optimal spatial scales for seasonal forecasts over Africa, Environ. Res. Lett., 15, 094023, https://doi.org/10.1088/1748-9326/ab9d38, 2020.
